ABSTRACT Anticoagulation in atrial fibrillation often relies on a fixed dose, infrequent dose adjustment and does not incorporate patient preferences for risks of stroke, bleeding, and death. We developed a Bayesian ML model (Adele) to guide individualized long-term dosing accounting for patient preferences. Adele is a Bayesian competing-risk, multistate hazard model trained on 5,380 edoxaban-treated AF patients with pharmacokinetic (PK) data from the ENGAGE AF-TIMI 48 trial.
